The impact of the State Innovation Models Initiative on population health |
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| Affiliation: | 1. Hunter College and NBER, Department of Economics, 695 Park Avenue, New York, NY 10065, United States;2. University of Michigan, School of Public Health, Department of Health Management and Policy, 1415 Washington Heights, Ann Arbor, MI 48109, United States;3. Visiting Nurse Service of New York, 220 East 42 Street, Floor 7, New York, NY 10017, United States;1. Centre for Human Drug Research, Leiden, Netherlands;2. Department of Internal Medicine, Leiden University Medical Centre, Leiden, Netherlands;3. Department of Pulmonary Diseases, VU University Medical Centre, Amsterdam, Netherlands;1. Surveillance and Health Services Research, American Cancer Society, Atlanta, Georgia;2. Department of Epidemiology and Biostatistics, University of Memphis School of Public Health, Memphis, Tennessee;3. Multidisciplinary Thoracic Oncology Program, Baptist Cancer Center, Memphis, Tennessee;1. Department of Geriatrics, College of Medicine, Florida State University, Tallahassee, FL;2. Department of Psychology, Buffalo State University, Buffalo, NY;1. Humana Healthcare Research, Louisville, KY, USA;2. Office of Health Affairs and Advocacy, Humana Inc., Louisville, KY, USA;3. Digital Health & Analytics, Humana Inc., Louisville, KY, USA;4. Trend Analytics & Forecasting, Humana Inc., Louisville, KY, USA;5. Division of Population Health, National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA;6. National Center for Chronic Disease Prevention and Health Promotion, Centers for Disease Control and Prevention, Atlanta, GA, USA |
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| Abstract: | In this paper, we examine the effects of the State Innovation Models Initiative (SIM) on population-level health status. SIM provided $250 million to six states in 2013 for broad delivery system reforms. We use data from the Behavioral Risk Factor Surveillance System for the years 2010–2016. Our sample is restricted to individuals ages 45 and older residing in 6 SIM and 15 control states. Treatment effects in a difference-in-difference design are estimated using a latent factor model for multiple indicators of health status. In addition to estimates for the primary sample, we obtain estimates for six subsamples based on strata of age, education, income, race and urban/rural status. We find that individuals in states that implemented SIM show significant improvements in health status. The effects of SIM are greater among older, Medicare eligible individuals, including those living in rural areas. The State Innovation Models Initiative, which provided financial incentives for states to implement health care delivery system reforms, led to population-level improvements in health status. |
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| Keywords: | Health care delivery Health care financing Medicare Latent factor models |
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